CDI-Type I: A science and engineering programming and inverse modeling environment for massively parallel heterogeneous computing systems

该项目的目标是为科学家和工程师提供一个通用的、独立于硬件的编程和运行时环境，该环境利用现有的各种可扩展、高性能、高度异构性的硬件系统，同时向程序员屏蔽每个系统的细节。该环境将进一步提供一个集成的逆建模工具，以执行指导性参数空间和不确定性分析以及模型优化。这种逆向建模对于确定一般模式至关重要，以便开发新的科学理论来表征复杂性，从而捕捉复杂的自然和工程系统的本质。除了提高我们对自然和建造系统的复杂性(主要的CDI主题)的理解外，拟议的环境将有助于从数据(次要的CDI主题)中可视化和提取知识，对于这些数据来说，逆方法(例如，反卷积、回归、参数空间/不确定性分析、模型优化)是必不可少的。然而，只有将可执行数千次模拟的性能最高的异类硬件解决方案与有效隐藏异类硬件复杂性的高级编程和逆建模环境相结合，才有可能实现这些目标。确定了几个可能受益于这种异类硬件实现的通用计算类别，以及来自科学和工程的示例应用程序。这样的实现将在科学和工程计算中开辟新的变革性机会，增加发现关于复杂的、易于反馈的系统中的多尺度相互作用、涌现行为、模式形成和自组织的新理论的可能性。几个基本的科学和工程计算类别以及来自地震学、火山学、流体力学和岩石磁学的示例应用已被确定为这种类型的异质计算的机会目标。

The goal of this project is to provide scientists and engineers with a generalized, hardware-independent programming and runtime environment that takes advantage of the wide range of scalable, high-performance, highly heterogeneous hardware systems available, while masking the details of each from the programmer. This environment will further provide an integrated inverse modeling tool to perform guided parameter space and uncertainty analysis as well as model optimization. Such inverse modeling is critical to identify general patterns in order to develop new scientific theories that characterize complexity and thus capture the essence of complex natural and engineered systems. In addition to improving our understanding of complexity in natural and built systems (primary CDI theme), the proposed environment will aid in visualizing and extracting knowledge from data (secondary CDI theme) for which inverse methods (e.g., deconvolutions, regressions, parameter space/uncertainty analysis, model optimization) are indispensable. Reaching these goals is only possible, however, when combining the highest-performing heterogeneous hardware solutions that can execute many thousands of simulations with a high-level programming and inverse modeling environment that effectively hides the heterogeneous hardware complexities. Several general computing classes are identified, with example applications from science and engineering, that are likely to benefit from such heterogeneous hardware implementations. Such implementations would then open up new, transformational opportunities in scientific and engineering computing, increasing the likelihood of discovery of new theories regarding multi-scale interactions, emergent behavior, pattern formation, and self-organization in complex, feedback-prone systems.Several fundamental scientific and engineering computing categories with example applications from seismology, volcanology, hydrodynamics, and rock magnetics have been identified as targets of opportunity for this type of heterogeneous computing.